Pournaras, E, Pilgerstorfer, P and Asikis, T (2018) Decentralized Collective Learning for Self-managed Sharing Economies. ACM Transactions on Autonomous and Adaptive Systems, 13 (2). pp. 1-33. ISSN 1556-4665
Abstract
The Internet of Things equips citizens with a phenomenal new means for online participation in sharing economies. When agents self-determine options from which they choose, for instance, their resource consumption and production, while these choices have a collective systemwide impact, optimal decision-making turns into a combinatorial optimization problem known as NP-hard. In such challenging computational problems, centrally managed (deep) learning systems often require personal data with implications on privacy and citizens’ autonomy. This article envisions an alternative unsupervised and decentralized collective learning approach that preserves privacy, autonomy, and participation of multi-agent systems self-organized into a hierarchical tree structure. Remote interactions orchestrate a highly efficient process for decentralized collective learning. This disruptive concept is realized by I-EPOS, the Iterative Economic Planning and Optimized Selections, accompanied by a paradigmatic software artifact. Strikingly, I-EPOS outperforms related algorithms that involve non-local brute-force operations or exchange full information. This article contributes new experimental findings about the influence of network topology and planning on learning efficiency as well as findings on techno-socio-economic tradeoffs and global optimality. Experimental evaluation with real-world data from energy and bike sharing pilots demonstrates the grand potential of collective learning to design ethically and socially responsible participatory sharing economies.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Copyright held by the owner/author(s). This is an author produced version of a paper published in ACM Transactions on Autonomous and Adaptive Systems. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Feb 2020 12:42 |
Last Modified: | 18 Feb 2020 12:42 |
Status: | Published |
Publisher: | Association for Computing Machinery (ACM) |
Identification Number: | 10.1145/3277668 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:157093 |